E1AR0002@SMUVM1.BITNET (Leff, Southern Methodist University) (03/06/87)
Seminar Announcement, Southern Methodist University, Department of Computer Science, Wednesday, Mar 11, 1987, 315 SIC, 1:30PM USING UNCERTAINTY TO SOLVE ANALOGIES David Rogers Cognitive Science and Machine Intelligence Laboratory University of Michigan Abstract Analogy involves the conceptual mapping of one situation onto another, assigning correspondences between objects in each situation. Uncertainty concerning the values of the objects' attributes or the correct category of an object is commonly considered a nuisance of little theoretical importance. In contrast, in this approach uncertainty is central: all attributes are to some degree uncertain, and category assignment of objects is fluid. Thanks to this all-pervading uncertainty (rather than dispite it), this architecture allows the system to represent the multiple, often conflicting pressures that guide our perceptions of situations in an analogy. Further, parallelism without global control is intrinsic in this architecture. Control is distributed throughout the system, at the level of its most primitive objects -- entities -- each entity communicating with a small number of other entities in the world. I will present a domain that uses deceptively simple strings of letters, followed by a description of the architecture used to solve problems in this domain. Finally, some results from a program written to implement these ideas will be presented.